title: Application of Kohonen neural networks for the non-morphological distinction between glomerular and tubular renal disease.
abstract: BACKGROUND
  A Kohonen topological map is an artificial intelligence system of the connectionist school (neural networks). The map learns the typical features of the subclasses in the learning set by means of a shortest Euclidean distance algorithm, after which self-adaptation of the neurons occurs. By its ability of self-organization and generalization, a Kohonen map is useful for pattern recognition, and its application in the medical field as an aid for decision making seems promising. This study describes the use of a Kohonen topological mapping system in the classification of renal diseases as being glomerular or tubular on basis of clinical characteristics and laboratory results.
  
  
  METHODS
  Forty-one parameters from 75 patients were retrospectively retrieved and used to train four different Kohonen maps of 10 x 10 neurons. For reference diagnostic classification, we referred to the results of the light-microscopic examination. The classification of the patients by the four different Kohonen networks was compared to the classification by a rule-based system and by three nephrologists. We also developed a 'hybrid' decision system that makes a classification on basis of the opinion of the four networks and that of the rule-based system.
  
  
  RESULTS
  The results show that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a higher sensitivity and predictive value than the nephrologists and the rule-based system, and that the best classification was performed by the hybrid system: sensitivity and predictive value for the diagnosis 'glomerular' respectively 100 and 88% for the network with the most adequate results, 90 and 83% for the nephrologists, 90 and 95% for the rule-based system, and 95 and 96% for the hybrid system; sensitivity and predictive value for the diagnosis 'tubular' respectively 50 and 100% for the neural networks, 31 and 45% for the nephrologists, 81 and 68% for the rule-based system, and 87 and 82% for the hybrid system).
  
  
  CONCLUSIONS
  We conclude that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a high sensitivity and predictive value. The rule-based system performs worse than the neural networks. The most adequate results were obtained with the hybrid system.
authors.affiliation: Department of Internal Medicine, University Hospital, Gent, Belgium.
authors.forename: W
authors.initials: W
authors.lastname: Van Biesen
authors.forename: G
authors.initials: G
authors.lastname: Sieben
authors.forename: N
authors.initials: N
authors.lastname: Lameire
authors.forename: R
authors.initials: R
authors.lastname: Vanholder
mesh_terms: D000328:Adult; D000368:Aged; D006801:Humans; D007674:Kidney Diseases / Q000145:classification*; D007678:Kidney Glomerulus; D007684:Kidney Tubules; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity